The first step in getting this accomplished is to download our banking data from our bank's website. Obviously, I am not going to describe how to do this for every bank's website, and you should look on your own bank's website, probably within the account movements section.

If you don't have a bank account, or your bank doesn't let you download your data, you shouldn't despair, just look at the additional material provided together with the book, and you will find a folder named data containing, among other things, an XLS file named banking. You can safely use this for your experiments.

Let's have a closer look at how our...

When moving to R, one of the common questions that arises is this, how do I produce a pivot table with R? Purists of the language will probably be horrified at this question, but we do not have to be too fussy: pivot tables are an effective and convenient way to summarize and show data, and are therefore relevant to be able to perform the same summarization in our beloved language.

As you might be guessing, yes, it is actually possible to perform the same kind of summarization, even if it is not called a pivot table. But before getting into detail, let's discuss the concept. What is a pivot table? 

We define with this concept a summary of a given detailed dataset, showing descriptive statistics of attributes stored within the dataset, aggregated by keys composed from other attributes of the same dataset.

To be clear, let&apos...

It is beyond the scope of this book to provide a comprehensive and exhaustive explanation of the data visualization principles and techniques, but in the remaining sections of this chapter, we are going to learn the basic elements of this powerful discipline and how to apply them to our data through the means of the ggplot2 package.

As is often the case, when dealing with data visualization we should start from the final objective to work out the best way to accomplish it. The main objective of data visualization is to effectively communicate an insight contained within a given set of data. We can elaborate a bit more on this. The point here...

• On the way our brain visualizes: Information Visualization by  Ware, Colin. (2012) , 3rd Edition. Morgan Kaufmann
• http://www.datavizcatalogue.com for quite an exhaustive catalogue of data visualization techniques and how to employ them
• Interaction of Color, Joseph Alber, one of the greatest books on the theory of colour 
• ggplot2: Elegant Graphics for Data Analysis, by Hadley Wickham, Springer-Verlag

Can you feel your backpack becoming heavy? This chapter was a big boost for your R knowledge: nearly 30 pages earlier you were only just aware of how to print "Hello World" with R, and now you have discovered useful insights from your real banking data.

We have learned the following:

• Installing additional packages in the base version of R 
• Importing data into your R environment
• Creating pivot tables in R
• Discovering and showing information through data visualization techniques
• Plotting data with ggplot2

I am tempted to accelerate further in the next chapter, immediately showing you how to implement data mining algorithms with the powerful weapon we have at our disposal. But, we have to be prudent and firmly cover the foundations to let you soundly build upon them. In the next chapter, we'll learn how to organize and conduct a data mining project through...